343 10 3494 https://omeka.ibu.edu.ba/files/original/77cb7e13b96d9afa8c0bbf3b0a8ce5b9.pdf 1f49214f1fcfe31a520f996a66583162 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Application of Machine Learning in Neuromarketing Research for the Analysis of Customer Preferences Author Author Admir Krilašević Abstract A summary of the resource. <p>Neuromarketing combines neuroscience and marketing to analyze consumer behavior through tools like electroencephalography (EEG), which captures subconscious and emotional responses. This thesis applies machine learning (ML) techniques to EEG data for predicting purchase decisions, addressing the limitations of traditional marketing methods. Using the NeuMa dataset, which includes EEG and eye-tracking data, key features such as frontal alpha asymmetry (FAA), power spectral density (PSD), and alpha-beta power ratios were extracted to build predictive models. Four ML algorithms—Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), and Convolutional Neural Networks (CNN)—were evaluated based on accuracy, ROC AUC, and execution time. SVM emerged as the best-performing model, achieving 94.3% accuracy. 99% ROC AUC, with efficient processing time, making it suitable for neuromarketing research. The results confirm the critical role of EEG features from the frontal region, particularly FAA and alpha-beta power ratios, in predicting consumer preferences. These metrics reflect emotional and subconscious responses, emphasizing their importance in purchase decisions. This study demonstrates the value of integrating EEG with ML for consumer analysis, offering a scalable, unbiased, and data-driven approach to marketing research. By combining neuroscience with modern methods, this research provides a foundation for improving consumer preference analysis. It highlights the potential of EEG-based metrics and ML models to enhance marketing strategies, moving beyond traditional self-report methods toward more objective and accurate insights.</p> Keywords Keywords. Neuromarketing, Machine Learning, EEG, Consumer Behaviour, Neural Networks, SVM, Frontal Alpha Asymmetr consumer behaviour EEG frontal alpha asymmetr machine learning master's thesis neural networks neuromarketing SVM https://omeka.ibu.edu.ba/files/original/b0fb3a777e0d8db43479c1568e3648d5.pdf 6bfa35ecaa5b9c1ceb5ab4ea446bcee7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sports Results Prediction Using Machine Learning Author Author Alden Obradović Abstract A summary of the resource. <p>Over recent decades, machine learning technology has increasingly been used to predict sports performance. The sports industry generates extensive statistical data on players, teams, and seasons. Traditional prediction methods have shown limited accuracy. With data mining, sports organizations have recognized the outdated analysis in their data and are now utilizing it effectively.</p> <p>The goal of this thesis is to explore accurate sports result predictions. Identifying significant features and analysing their impact on match outcomes is essential. Key variables include team statistics, player statistics, and historical data. These factors help managers and club directors forecast match winners and determine strategies. Machine learning techniques like KNN, Random Forest, logistic regression, and SVM are often applied to predict match results.</p> <p>These predictions help coaches and managers assess player performance, evaluate skills, anticipate injuries, and strategize for upcoming games. Additionally, accurate predictions have significantly fuelled the sports betting industry, which is expanding rapidly thanks to the convenience of mobile and tablet devices.<br /><br />This research proposes an AI-based framework to predict football match outcomes. Itexamines the effectiveness of system learning algorithms and reviews data mining techniques for predicting sports performance, highlighting their strengths and weaknesses. Despite previous research attempts, achieving high precision in game result predictions remains challenging.</p> Keywords Keywords. Machine Learning, Sport Results, Prediction machine learning master's thesis prediction sport results https://omeka.ibu.edu.ba/files/original/32457844bdc2553cb82d2f01f486d89e.pdf 31a94ce65e9b2acb9f5da0be97ca7c0e Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Master's Theses Description An account of the resource IT Master's Thesis collection features master's theses authored by graduate students in the Department of Information Technology. Each thesis reflects a significant research effort, combining theoretical knowledge with practical application to address complex challenges in the IT domain. These works demonstrate students’ advanced understanding of information systems, software engineering, data science, cybersecurity, and emerging technologies. The theses serve as a testament to the students' capability to conduct independent research, propose innovative solutions, and contribute to the advancement of the IT field. Subject The topic of the resource IT Master's Thesis Language A language of the resource English Language Author Author IT Department Master’s Students Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Credit Card Fraud Detection Using Machine Learning Algorithms and Data Analysis Techniques Author Author Sarah Kozić Abstract A summary of the resource. In today’s world usage of card-based and online payment methods is rapidly increasing, and with this growth comes the issue of cybersecurity and overall fraud. The credit card fraud rate has never been higher, and it is following a growing trend.<br /><br />Therefore, improvement of credit card fraud detection systems is the main priority for all banks, systems that are providing credit card-based payments and all the participants in the digital payments market. This also comes for the purpose of the large percentage of the population that is using their credit cards daily, from everyday payments to international transactions that are of great value.<br /><br />The goal was to train multiple models to define if referenced transactions should be treated as fraud, and the results were measured by standard machine learning parameters. The model that had best results is Ensembled model using Decision Tree, Logistic Regression and K-Nearest Neighbor models with overall accuracy of 99.91% with Feature Selection algorithm applied. Ensemble method combines multiple models and creates the model with the best metrics possible. Along with this model, we have trained Logistic Regression model, K-Nearest Neighbors, Support Vectors Machines and Neural Networks, with accuracies respectively 88.37%, 85.48%, 00.73% and 98.11% with features selected.<br /><br />This research also covers the part of data preprocessing, as this step is crucial when building a model for credit card fraud detection systems. These systems must be fast and precise in order to be usable, as they are dealing with large sets of imbalanced data.<br /> <p>At the end of the study, individuals will have better insight in credit card transactions, will also be familiar with the different methods for detecting credit card frauds and will have insight in which model suits the needs of this case the most.</p> Keywords Keywords. Credit Card, Fraud, Transaction, Machine Learning Algorithms, Classification, Dataset Preprocessing algorithms classification credit card dataset preprocessing fraud machine learning master's thesis transaction https://omeka.ibu.edu.ba/files/original/f6920e7597178a744db73ca7a65acede.pdf c2b6a783fa42ff5765c2eb66aafc6a50 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BOOKS Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Transgressive Space in Literature: Theory in Practice Author Author Ibrahim Murat Öner Publisher An entity responsible for making the resource available International Burch University https://omeka.ibu.edu.ba/files/original/a74ee21684653438ceb8f4a099aa8ee2.pdf 6804f2ce63e462935b4d6b7d4dabafee Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sunglasses E-commerce Webshop With Credit Card Payment Author Author Ilma Hodžić Abstract A summary of the resource. The Sunglasses E-commerce Webshop project addresses the need for a modern online platform to sell eyewear products, specifically sunglasses, to a wide customer base. Problem Statement - Traditional brick-and-mortar sunglasses retailers are limited by geography and operating hours, whereas an e-commerce solution can provide 24/7 access and a broader reach. This project’s objective was to design and implement a secure, user-friendly web application for browsing and purchasing sunglasses online. Methods - The development followed a structured approach including requirement analysis, system design with UML diagrams, iterative implementation, and rigorous testing. Key technologies used include Angular for the frontend, ASP.NET Core Web API for the backend, and MSSQL for data storage. The system integrates the Stripe API in sandbox mode to handle payments safely, avoiding actual charges during development. Modern development practices such as responsive design, RESTful APIs, and MVC architecture were employed to ensure scalability and maintainability. Results - The resulting application allows users to register and log in, browse products with filtering options, add items to a shopping cart, and checkout securely using test credit card data. An administrative module enables product and order management. System testing indicates that all major use cases - from account creation to order placement - perform as expected, and the Stripe integration successfully simulates payment transactions without processing real money. Conclusion - The project demonstrates the successful creation of a specialized e-commerce platform for sunglasses. It offers a convenient shopping experience for users and an effective sales channel for the business. Future improvements, such as integrating augmented reality “virtual try-on” features and expanding product recommendations, are suggested to further enhance user engagement. Overall, the project showcases how a full-stack web application can meet real-world requirements for online retail, and it highlights lessons in web security, payment integration, and user experience design. Keywords Keywords. E-commerce, Webshop, Sunglasses, Stripe API, Payment Gateway, Web Development, UML, Online Shopping Language A language of the resource English language e-commerce sunglasses web development https://omeka.ibu.edu.ba/files/original/06e97e59b1196ae4206d8a400a77b262.pdf 8d846e47c333719dbd78fd4d92de9253 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Cinebh - Cinema Management System Author Author Sead Mašetić Abstract A summary of the resource. Cinema management involves handling reservations, scheduling screenings, and maintaining effective communication with customers. This project presents a Cinema Management System designed to automate these processes, reducing administrative workload and enhancing user experience. The system streamlines reservations, sends automated notifications for confirmations and reminders, and provides an intuitive interface for both customers and administrators. Developed using Spring Boot [6] for the backend and React [7] for the frontend, the system ensures scalability and maintainability. The development process followed a structured approach, starting with requirement analysis to identify challenges in manual cinema operations. The system was designed using a three-tier architecture, with a Spring Boot [6] backend handling authentication, reservations, and notifications, a React [7] frontend providing an intuitive interface, and a PostgreSQL [4] database for efficient data storage. Automated email and in-app notifications were integrated to keep users informed about their bookings. The Cinema Management System successfully automates key aspects of cinema operations, reducing manual workload and improving user experience. Testing confirmed the platform's efficiency, user-friendliness, and scalability, with responsive performance across different devices. By integrating automation and a modern technology stack, the system significantly improves operational efficiency for cinemas. Keywords Keywords. Cinema Management, Reservations, Real-Time Notifications, WebSockets, Venues Language A language of the resource English language cinema Management reservations software engineering https://omeka.ibu.edu.ba/files/original/bccd80fa498d156131e0adb3ba75c90f.pdf d657b356627bba519e8c11e21e879df3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis of Youtube Comments and Comparison of Different Machine Learning Models Author Author Dario Petric Abstract A summary of the resource. Every day, a large number of media is shared on social media websites, as well as others sharing their opinions and sentiments on said posts. In this project, the main goal was to examine to what degree traditional machine learning algorithms can be used to classify and examine the sentiment of YouTube comments—are they positive, negative, or neutral? Because comments are generally brief, full of slang terms, emojis, and even misspellings on some occasions, this type of assignment can be difficult. To address this, a dataset of YouTube comments labeled for sentiment was acquired. The data was preprocessed by removing stopwords and punctuation, transforming the text into numbers using the TF-IDF method, and various models such as Logistic Regression, Naive Bayes, Decision Tree, and Support Vector Machine (SVM) were trained. The performance of each model was compared, on metrics such as accuracy, precision, recall, and F1-score. Overall, the project helped demonstrate that with proper precautions in place, even some of the older machine learning algorithms can be effective at determining sentiment in random real-world text like YouTube comments. It also allowed for a better understanding of how important preprocessing really is when working with text data. Keywords Keywords. Sentiment analysis, YouTube comments, natural language processing, machine learning, logistic regression, support vector machine, random forest, SMOTE, BERT, deep learning, text classification, feature extraction, TF-IDF, data preprocessing, model comparison, class imbalance, evaluation metrics, confusion matrix Language A language of the resource English language https://omeka.ibu.edu.ba/files/original/f1eee7528cda8518e82e55c91b9b45ca.pdf 186db24c8ce72fe64dfbf47aa740daab Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Tire Shop E-commerce Platform Author Author Emina Omerčević Abstract A summary of the resource. The TireShop project addresses the need for a modern, efficient online platform for buying and managing tire purchases. Customers often struggle with outdated websites, lack of product filtering options, and inefficient checkout processes. TireShop aims to solve these issues by offering a user-friendly experience with product listing, sorting, filtering, cart functionality, checkout, and personalized AI-based product recommendations. The project leverages a powerful technology stack, combining Angular for the frontend and .NET 8 for the backend, with MS SQL Server for data persistence. Key features include user authentication with password hashing, a fully functional shopping cart, admin dashboards for product management, and integration with SendGrid for real-time email notifications. Additionally, AI recommendations powered by Groq API enhance user experience by suggesting similar products. The result is a responsive, scalable, and secure e-commerce application tailored to the tire retail market. Keywords Keywords. TireShop, e-commerce, .NET 8, Angular, SendGrid, Groq AI, Stripe Language A language of the resource English language https://omeka.ibu.edu.ba/files/original/f125f963027133e5c69eec9f67755dab.pdf 344a08b79c4366bc6c708180fff708c2 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource BnBosnia - Booking platform for Bosnia and Herzegovina Author Author Orhan Huseinbegović Abstract A summary of the resource. The objective of this project is to develop a booking platform, crafted for the needs of Bosnia and Herzegovina, that is designed to help its users to rent and book properties. Technologies that are used to make this platform are React.js for frontend side along with Node.js powered with Express framework on the backend side, while utilizing PostgreSQL for database. The problem addressed is the absence of a booking platform specialised and tailored for the needs of the country Bosnia and Herzegovina. A technique that can be used is the creation of a web application that provides its users with seamless and free access to a booking system. On this web application, users can search for properties to book, publish their own properties and make profit from it, save properties for future booking, and much more. The results include a fully functional web application with a simple UI and a great UX, that attracts foreign and local tourists to use the application. The conclusion drawn is that the proposed BnBosnia booking platform, effectively helps their users to book accommodation and rent their own accommodation, allowing them to make profit. Keywords Keywords. booking platform, Bosnia and Herzegovina, property renting Language A language of the resource English language booking software engineering tourism https://omeka.ibu.edu.ba/files/original/8bbbfa2647fe7199e867342582c4115a.pdf 19fc0a50315eee42be7d7cfae034c9d8 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource IT Senior Design Projects Description An account of the resource The IT Senior Design Projects (SDPs) category showcases innovative and practical final-year capstone projects developed by undergraduate and graduate students in the field of Information Technology. These projects represent the culmination of students' academic and technical expertise, demonstrating their ability to solve real-world problems through software and hardware solutions. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparing Sentiment Analysis Emotion Classification Models for Psychotherapy Use Author Author Anja Šehovac Abstract A summary of the resource. Sentiment analysis could be a powerful tool in evolving psychotherapy. There is a rapid increase in patients seeking mental health help, and NLP could help make their experience more accessible and efficient. A sentiment analysis-based journal could help users track their thought patterns, their severity, and progress over time. This paper investigates the effectiveness of Naive Bayes, Random Forest, Support Vector Machine, XGBoost and BERT algorithms paired with TF-IDF, Bag of Words, One-Hot Encoding and Word2Vec feature extraction algorithms in emotion classification of text for future journal use. Comparative analysis helps understand which algorithms could be best suited for this type of multi-label classification, and broadens current research by testing several algorithms, which can show what should be further worked upon in the field, and which algorithms are best to avoid. Many studies test only one or two algorithms, leaving less room for comparison on the same dataset and under the same conditions, so it is unclear if the accuracy differences in different studies are derived from a better model or a better dataset. Furthermore, other studies do not provide a comparative analysis of feature extraction models. The four machine learning algorithms were trained on a dataset of 17.449 emotion-annotated sentences after preprocessing steps including tokenization, lemmatization, and vectorization for feature extraction. Out of classical models, Naive Bayes performed the worst with a 76% accuracy, and XGBoost performed the best with a 0.88 accuracy. Furthermore, BERT accomplished 93% accuracy, making it the best performing model in the study. Each algorithm performed better with a different vectorization method. This shows improvement over other research in the field, and the potential of sentiment analysis in aiding psychotherapy needs. Keywords Keywords. Random Forest, Support Vector Machine, XGBoost, Naıve Bayes, BERT, sentiment analysis, psychotherapy, journal, emotion classification, TF-IDF, Bag of Words, One-Hot Encoding. Word2Vec Language A language of the resource English language classification models data science emotions sentiment analysis